Welcome to Debtech International


Onsite Seminar

Logical Data Modeling


This course is about taking knowledge of the business and its rules and converting these into a stable data model. The data model is a representation of the objects that the business uses, the characteristics of those objects and the rules that govern their relationship

Objectives
Upon completion of this course attendees will produce models that are:

  • Independent of implementation and organizational structure
  • Accurate representation of the business
  • Stable
  • Simple (because they use refinement)
  • Appropriately scoped
  • Based on sound theoretical principles
  • Easy to understand.

This is a pragmatic workshop. There are many exercises and one continuous case study. This course offers comprehensive coverage of mainstream data modeling concepts. You learn a rigorous method for defining data. You also learn how to gather the data, define and analyze business rules, perform normalization, and use the results to create a stable model of the data within a business area You learn state of the art refinement techniques like subtyping and recursive relationships. Above all, you learn how to do data modeling rapidly.
Proven techniques are stressed for normalization, data model creation, interaction of data and process models and data views analysis. Both top-down and bottom-up methods are used for data model creation.
A practical case study is used throughout the workshop.  The case study provides experience with data identification, normalization, detailed data identification and model verification.  It is intended for students who have no knowledge of data modeling.

Prerequisite
This class is intended for students who have no knowledge of data modeling.

Course Outline

Introduction

  • What is Data Modeling
  • Why use Data Modeling
  • The benefits of Data Modeling
  • Overall development framework
  • Stages of development
  • The kinds of projects
  • Data driven development
  • Modeling concepts
  • Data modeling
  • Process modeling
  • Usage modeling (model interaction)
  • Characteristics of good models

High Level Data Modeling

  • Introduction to data modeling
  • Brainstorming business rules, entities and relationships
  • Rules for the High Level Data Model
  • Explanation of major objects
  • Entities, Attributes, Relationships
  • Business rules
  • Multiple relationships
  • Recursive relationship
  • Purpose of high level: Scope, management review, top-down framework
  • Finding primary entities
  • Defining relationships
  • Validating entities
  • Identifying keys
  • EXERCISE:  High level data modeling 

Detailed Data Modeling

  • Model expansion
  • Detailed modeling constructs
  • Methods of Model Expansion
  • Types of Data
  • Types of Keys
  • Types of Entities
  • EXERCISE:  Model expansion

Normalization

  • What normalization is
  • What normalization is not
  • Rules and steps of normalization
  • Practical tips for normalization
  • EXERCISE: Mini-exercise
  • EXERCISE: Case study

Views Analysis

  •  Definition of a data view
  • Sources of data views of data
  • Importance of views
  • Results of views analysis
  • EXERCISE:  Data views for case study

Current Systems Analysis

  • Reasons for doing current systems analysis
  • Analyzing current data
  • Problems in current data analysis
  • Analyzing current processes
  • Importance of current systems analysis

Model Consolidation

  • Reality of separate model development
  • Importance of integration
  • Rules for integration
  • Conflict resolution

Data Model Refinement

  • Abstraction:  generalization and aggregation
  • Subtyping
  • Aggregation
  • Bill of materials
  • Derived data
  • Change data
  • Modeling goals
  • Modeling time
  • Final model stabilization
  • EXERCISE: Model refinement in case study

Model Interaction

  • The importance of model interaction
  • Issues in model interaction
  • Integrating models via matrices
  • Integrating models via maps
  • Integrating models via views
  • Other validations and cross-checks

EXERCISE:  Data usage mapping
Preparing for Design

  • Phase review
  • Review participants
  • Goals of phase review
  • Introduction to design
  • Purpose of design
  • Steps of design
  • Safe data design trade-offs
  • Aggressive data design trade-offs

Duration
3 days

Course Format
Lecture, group discussion and exercises

Instructor
Tom Haughey

To request a quote for this in-house seminar
Please call (561) 218-4752 or email info@debtechint.com

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